Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
def getDistortionCoefs(show_result=False):
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
if(show_result):
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (9,6), corners, ret)
plt.imshow(img)
plt.show()
size = cv2.imread(images[0]).shape[0:2]
return cv2.calibrateCamera(objpoints, imgpoints, size, None, None)
Calibrate camera¶
In [2]:
ret, mtx, dist, rvecs, tvecs = getDistortionCoefs(True)
Undistort¶
In [3]:
#img1 = cv2.imread('camera_cal/calibration1.jpg')
img1 = cv2.imread('test_images/test4.jpg')
img2 = cv2.undistort(img1, mtx, dist, None, mtx)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(img2)
ax2.set_title('Undistorted Image', fontsize=30)
# cv2.imwrite('calibresult1.png',img1)
# cv2.imwrite('calibresult2.png',img2)
Out[3]:
Perspective transform¶
In [4]:
trans_test = cv2.imread('test_images/straight_lines1.jpg')
trans_test = cv2.undistort(trans_test, mtx, dist, None, mtx)
plt.imshow(trans_test)
# plt.plot(250, 710, ".")
# plt.plot(1130, 710, ".")
# plt.plot(570, 470, ".")
# plt.plot(740, 470, ".")
# plt.plot(585, 460, ".")
# plt.plot(203, 720, ".")
# plt.plot(1127, 720, ".")
# plt.plot(695, 460, ".")
plt.plot(585, 460, ".")
plt.plot(203, 720, ".")
plt.plot(1100, 720, ".")
plt.plot(695, 460, ".")
# plt.plot(320, 0, ".")
# plt.plot(320, 720, ".")
# plt.plot(960, 720, ".")
# plt.plot(960, 0, ".")
Out[4]:
In [5]:
def warp(img):
img_size = (img.shape[1],img.shape[0])
src = np.float32([
[585, 460],
[203, 720],
[1100, 720],
[695, 460]
])
dst = np.float32([
[320, 0],
[320, 720],
[960, 720],
[960, 0]
])
# Compute the perspective transform, M, given source and destination points:
M = cv2.getPerspectiveTransform(src, dst)
#Compute the inverse perspective transform:
Minv = cv2.getPerspectiveTransform(dst, src)
#Warp an image using the perspective transform, M:
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
return warped, Minv
In [6]:
%matplotlib inline
img1 = mpimg.imread('test_images/straight_lines1.jpg')
img1 = cv2.undistort(img1, mtx, dist, None, mtx)
img2, Minv = warp(img1)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(img2)
ax2.set_title('Warped Image', fontsize=30)
Out[6]:
Sobel¶
In [7]:
# Define a function that takes an image, gradient orientation,
# and threshold min / max values.
def abs_sobel_thresh(img, sobel_kernel=3, orient='x', mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Apply x or y gradient with the OpenCV Sobel() function
# and take the absolute value
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
if orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
# Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
binary_output[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1
# Return the result
return binary_output
# Define a function to threshold an image for a given range and Sobel kernel
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
# Grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Calculate the x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute value of the gradient direction,
# apply a threshold, and create a binary image result
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# Return the binary image
return binary_output
# Define a function to return the magnitude of the gradient
# for a given sobel kernel size and threshold values
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Take both Sobel x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
gradmag = np.sqrt(sobelx**2 + sobely**2)
# Rescale to 8 bit
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
# Create a binary image of ones where threshold is met, zeros otherwise
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
# Return the binary image
return binary_output
def combo(image):
# Choose a Sobel kernel size
ksize = 15 # Choose a larger odd number to smooth gradient measurements
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(image, sobel_kernel=3, orient='x', mag_thresh=(10, 100))
grady = abs_sobel_thresh(image, sobel_kernel=3, orient='y', mag_thresh=(10, 100))
mag_binary = mag_thresh(image, sobel_kernel=9, mag_thresh=(30, 100))
dir_binary = dir_threshold(image, sobel_kernel=9, thresh=(0.7, 1.3))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
return combined
In [8]:
image = cv2.imread('test_images/test4.jpg')
# Run the function
grad_binary = combo(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Color threshold¶
In [9]:
def colorThreshold(img):
# Convert to HLS color space and separate the S channel
# Note: img is the undistorted image
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
# Grayscale image
# NOTE: we already saw that standard grayscaling lost color information for the lane lines
# Explore gradients in other colors spaces / color channels to see what might work better
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Sobel x
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0) # Take the derivative in x
abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
# Threshold x gradient
thresh_min = 20
thresh_max = 100
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
# Threshold color channel
s_thresh_min = 170
s_thresh_max = 255
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary))
# Combine the two binary thresholds
combined_binary = np.zeros_like(sxbinary)
combined_binary[(s_binary == 1) | (sxbinary == 1)] = 1
return combined_binary
In [10]:
image = cv2.imread('test_images/test4.jpg')
# Run the function
grad_binary = colorThreshold(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Thresholded Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Locate the Lane Lines and Fit a Polynomial¶
In [11]:
def getLinesFit(binary_warped, show_result=False):
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[int(binary_warped.shape[0]/2):,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
if(show_result):
# VISUALIZE
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
return (left_fit, right_fit), (leftx, lefty), (rightx, righty)
def getNextLinesFit(binary_warped, left_fit, right_fit, show_result = False):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
if(show_result):
# VISUALIZE
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
return (left_fit, right_fit), (leftx, lefty), (rightx, righty)
Measuring Curvature¶
In [12]:
def getCurvature(left_fit, right_fit, leftLine, rightLine, img):
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(leftLine[1]*ym_per_pix, leftLine[0]*xm_per_pix, 2)
right_fit_cr = np.polyfit(rightLine[1]*ym_per_pix, rightLine[0]*xm_per_pix, 2)
shape = img.shape[0:2]
left_line_y = np.polyval(left_fit_cr, shape[0])
right_line_y = np.polyval(right_fit_cr, shape[0])
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*left_line_y*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*right_line_y*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
# Now our radius of curvature is in meters
return (left_curverad, right_curverad)
Find car position¶
In [13]:
def findCarPosition(img, curve):
shape = img.shape[0:2]
left_line_y = np.polyval(curve[0], shape[0])
right_line_y = np.polyval(curve[1], shape[0])
lane_center = left_line_y + (right_line_y - left_line_y) / 2
car_to_lane = shape[1] / 2 - lane_center
car_to_lane_m = round(abs(car_to_lane * 3.7/720),2) # in meters
position = "center"
if(car_to_lane < 0):
position = "left"
elif (car_to_lane > 0):
position = "right"
#print(position, car_to_lane, car_to_lane_m,"m")
#print(left_line_y, right_line_y, lane_center)
return car_to_lane_m, position
Pipeline¶
In [14]:
def drawResults(undist, warped, Minv, left_fit, right_fit):
# Generate x and y values for plotting
ploty = np.linspace(0, warped.shape[0]-1, warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw the lines on
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (warped.shape[1], warped.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
return result
In [15]:
def pipeline(img1, curve=None, show_result = False):
# 1. camera calibration matrix and distortion coefficients
#ret, mtx, dist, rvecs, tvecs = getDistortionCoefs(show_result)
# 2. apply them to undistort each new frame
img2 = cv2.undistort(img1, mtx, dist, None, mtx)
# 3. apply thresholds to create a binary image
img2 = colorThreshold(img2)
# 4. apply a perspective transform.
img2, Minv = warp(img2)
if(show_result):
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(img2, cmap='gray')
ax2.set_title('Result', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.show()
# 5. Locate the Lane Lines and Fit a Polynomial
leftLine=None
rightLine=None
if(curve is None):
# get starting curve
curve, leftLine, rightLine = getLinesFit(img2, show_result)
else:
# continue with existing curve
curve, leftLine, rightLine = getNextLinesFit(img2, curve[0], curve[1], show_result)
if(show_result):
plt.show()
# 6. Find vehicle position
position_m, position_side = findCarPosition(img1, curve)
if(show_result):
print(position_m, 'm', position_side)
# 7. Measuring Curvature
curvature = getCurvature(curve[0], curve[1], leftLine, rightLine, img1)
if(show_result):
print(curvature[0], 'm', curvature[1], 'm')
# 8. Show results
result = drawResults(img1, img2, Minv, curve[0], curve[1])
title = "radius of curvature: " + "(L):" + str(round(curvature[0],2)) + "m." + " (R):" + str(round(curvature[1],2)) + "m."
title += ", offset: " + str(position_m) + 'm ' + str(position_side)
cv2.putText(result, title, (10,50), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2)
return result, curve
In [16]:
images = glob.glob('test_images/test*.jpg')
images.append('test_images/straight_lines1.jpg')
images.append('test_images/straight_lines2.jpg')
for image in images:
result, curve = pipeline(img1 = cv2.imread(image), show_result = True)
plt.imshow(result)
plt.show()
Process video¶
In [17]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from moviepy.editor import ImageSequenceClip
from IPython.display import HTML
def process_video(inputVideo, outputVideo):
images_list = []
curve = None
clip = VideoFileClip(inputVideo)
for index, frame in enumerate(clip.iter_frames(progress_bar=True)):
if (index % 1 == 0):
frame, curve = pipeline(img1 = frame, curve = curve, show_result = False)
images_list.append(frame)
white_clip = ImageSequenceClip(images_list, fps=int(len(images_list) / clip.duration))
%time white_clip.write_videofile(outputVideo, audio=False)
In [18]:
process_video("project_video.mp4", 'test_videos/project_video_result.mp4')
In [19]:
# process_video('test_videos/challenge_video_result.mp4', "challenge_video_result.mp4")
In [20]:
# process_video('test_videos/harder_challenge_video_result.mp4', "harder_challenge_video_result.mp4")
In [21]:
white_output = 'test_videos/project_video_result.mp4'
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
Out[21]:
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